This course is intended as a first step for learners who seek to become producers of social science research. It is organized as an introduction to the design and execution of a research study. It introduces the key elements of a proposal for a research study, and explains the role of each. It reviews the major types of qualitative and quantitative data used in social science research, and then introduces some of the most important sources of existing data available freely or by application, worldwide and for China. The course offers an overview of basic principles in the design of surveys, including a brief introduction to sampling. Basic techniques for quantitative analysis are also introduced, along with a review of common challenges that arise in the interpretation of results. Professional and ethical issues that often arise in the conduct of research are also discussed. The course concludes with an introduction to the options for further study available to the interested student, and an overview of the key steps involved in selecting postgraduate programs and applying for admission. Learners who complete the course will be able to make an informed decision about whether to pursue advanced studies, and should be adequately prepared to write an application for postgraduate study that exhibits basic understanding of key aspects of social science research paradigms and methodologies.
Explore the big questions in social science and learn how you can be a producer of social science research.
Course Overview video: https://youtu.be/QuMOAlwhpvU
Part 1 should be completed before taking this course: https://www.coursera.org/learn/social-science-study-chinese-society

教学方

Cameron Campbell

Professor of Social Science

脚本

[MUSIC] Hi, in this lecture I'm going to be talking about quantitative analysis. I won't be trying to teach you the methods themselves. You'll have to take a statistics course for that. Rather, I'll be focusing on some conceptual issues that we have to think about when we're designing our studies, and then interpreting the results. I'll start by talking about tabulation, the most basic form of quantitative analysis. And I'll focus in particular on the design of tables and how we present them to an audience, to maximize the clarity of the implications. Then I'll talk about correlation and regression. I'll try to give you a basic conceptual understanding of what they are, what the measurements really represent. Then I'll turn to the phenomenon of regression to the mean. Something that we have to keep in mind when we're interpreting the results from certain kinds of studies. Then I'll talk about statistical significance. It's an often misunderstood, or misused, tool. So I'll be talking about the situations where we can usefully make use of statistical significance, and other situations where we have to be more careful. I'll talk about type I errors, the situation where, in conducting a test of statistical significance, we incorrectly accept the existence of a relationship, a difference, when in fact there isn't one. We refer to that as a false positive. And I'll talk about the scenario of type II errors, where perhaps because of a overly small sample or for some other reason, we fail to detect a relationship or a difference that actually is there in the population that we're trying to study. So overall, we have three goals. The first is to introduce some very basic methods for quantitative analysis. Of course if you really want to learn these techniques in depth, you're going to have to take a statistics course. All I can give here is a taste. Then I'll identify some key issues in the interpretation of results. Especially when we get to the issues of correlation and regression. Regression of the mean. And statistical significance. And then finally I'll discuss some of the implications for all of this, for the way we design our studies. I look forward to sharing this information with you in the remaining modules in this lecture.